Regularised estimation of 2D-locally stationary wavelet processes

Gibberd, A. J. and Nelson, J. D. B. (2016) Regularised estimation of 2D-locally stationary wavelet processes. In: 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE. ISBN 9781467378048

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Abstract

Locally Stationary Wavelet processes provide a flexible way of describing the time/space evolution of autocovariance structure over an ordered field such as an image/time-series. Classically, estimation of such models assume continuous smoothness of the underlying spectra and are estimated via local kernel smoothers. We propose a new model which permits spectral jumps, and suggest a regularised estimator and algorithm which can recover such structure from images. We demonstrate the effectiveness of our method in a synthetic experiment where it shows desirable estimation properties. We conclude with an application to real images which illustrate the qualitative difference between the proposed and previous methods.

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Contribution in Book/Report/Proceedings
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ID Code:
128566
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Deposited On:
06 Nov 2018 15:28
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Nov 2020 11:45